org.netlib.lapack
Class DSTEVR

java.lang.Object
org.netlib.lapack.DSTEVR

public class DSTEVR

extends java.lang.Object

DSTEVR is a simplified interface to the JLAPACK routine dstevr.
This interface converts Java-style 2D row-major arrays into
the 1D column-major linearized arrays expected by the lower
level JLAPACK routines. Using this interface also allows you
to omit offset and leading dimension arguments. However, because
of these conversions, these routines will be slower than the low
level ones. Following is the description from the original Fortran
source. Contact seymour@cs.utk.edu with any questions.

* ..
*
* Purpose
* =======
*
* DSTEVR computes selected eigenvalues and, optionally, eigenvectors
* of a real symmetric tridiagonal matrix T. Eigenvalues and
* eigenvectors can be selected by specifying either a range of values
* or a range of indices for the desired eigenvalues.
*
* Whenever possible, DSTEVR calls SSTEGR to compute the
* eigenspectrum using Relatively Robust Representations. DSTEGR
* computes eigenvalues by the dqds algorithm, while orthogonal
* eigenvectors are computed from various "good" L D L^T representations
* (also known as Relatively Robust Representations). Gram-Schmidt
* orthogonalization is avoided as far as possible. More specifically,
* the various steps of the algorithm are as follows. For the i-th
* unreduced block of T,
* (a) Compute T - sigma_i = L_i D_i L_i^T, such that L_i D_i L_i^T
* is a relatively robust representation,
* (b) Compute the eigenvalues, lambda_j, of L_i D_i L_i^T to high
* relative accuracy by the dqds algorithm,
* (c) If there is a cluster of close eigenvalues, "choose" sigma_i
* close to the cluster, and go to step (a),
* (d) Given the approximate eigenvalue lambda_j of L_i D_i L_i^T,
* compute the corresponding eigenvector by forming a
* rank-revealing twisted factorization.
* The desired accuracy of the output can be specified by the input
* parameter ABSTOL.
*
* For more details, see "A new O(n^2) algorithm for the symmetric
* tridiagonal eigenvalue/eigenvector problem", by Inderjit Dhillon,
* Computer Science Division Technical Report No. UCB//CSD-97-971,
* UC Berkeley, May 1997.
*
*
* Note 1 : DSTEVR calls SSTEGR when the full spectrum is requested
* on machines which conform to the ieee-754 floating point standard.
* DSTEVR calls SSTEBZ and SSTEIN on non-ieee machines and
* when partial spectrum requests are made.
*
* Normal execution of DSTEGR may create NaNs and infinities and
* hence may abort due to a floating point exception in environments
* which do not handle NaNs and infinities in the ieee standard default
* manner.
*
* Arguments
* =========
*
* JOBZ (input) CHARACTER*1
* = 'N': Compute eigenvalues only;
* = 'V': Compute eigenvalues and eigenvectors.
*
* RANGE (input) CHARACTER*1
* = 'A': all eigenvalues will be found.
* = 'V': all eigenvalues in the half-open interval (VL,VU]
* will be found.
* = 'I': the IL-th through IU-th eigenvalues will be found.
********** For RANGE = 'V' or 'I' and IU - IL < N - 1, DSTEBZ and
********** DSTEIN are called
*
* N (input) INTEGER
* The order of the matrix. N >= 0.
*
* D (input/output) DOUBLE PRECISION array, dimension (N)
* On entry, the n diagonal elements of the tridiagonal matrix
* A.
* On exit, D may be multiplied by a constant factor chosen
* to avoid over/underflow in computing the eigenvalues.
*
* E (input/output) DOUBLE PRECISION array, dimension (N)
* On entry, the (n-1) subdiagonal elements of the tridiagonal
* matrix A in elements 1 to N-1 of E; E(N) need not be set.
* On exit, E may be multiplied by a constant factor chosen
* to avoid over/underflow in computing the eigenvalues.
*
* VL (input) DOUBLE PRECISION
* VU (input) DOUBLE PRECISION
* If RANGE='V', the lower and upper bounds of the interval to
* be searched for eigenvalues. VL < VU.
* Not referenced if RANGE = 'A' or 'I'.
*
* IL (input) INTEGER
* IU (input) INTEGER
* If RANGE='I', the indices (in ascending order) of the
* smallest and largest eigenvalues to be returned.
* 1 <= IL <= IU <= N, if N > 0; IL = 1 and IU = 0 if N = 0.
* Not referenced if RANGE = 'A' or 'V'.
*
* ABSTOL (input) DOUBLE PRECISION
* The absolute error tolerance for the eigenvalues.
* An approximate eigenvalue is accepted as converged
* when it is determined to lie in an interval [a,b]
* of width less than or equal to
*
* ABSTOL + EPS * max( |a|,|b| ) ,
*
* where EPS is the machine precision. If ABSTOL is less than
* or equal to zero, then EPS*|T| will be used in its place,
* where |T| is the 1-norm of the tridiagonal matrix obtained
* by reducing A to tridiagonal form.
*
* See "Computing Small Singular Values of Bidiagonal Matrices
* with Guaranteed High Relative Accuracy," by Demmel and
* Kahan, LAPACK Working Note #3.
*
* If high relative accuracy is important, set ABSTOL to
* DLAMCH( 'Safe minimum' ). Doing so will guarantee that
* eigenvalues are computed to high relative accuracy when
* possible in future releases. The current code does not
* make any guarantees about high relative accuracy, but
* future releases will. See J. Barlow and J. Demmel,
* "Computing Accurate Eigensystems of Scaled Diagonally
* Dominant Matrices", LAPACK Working Note #7, for a discussion
* of which matrices define their eigenvalues to high relative
* accuracy.
*
* M (output) INTEGER
* The total number of eigenvalues found. 0 <= M <= N.
* If RANGE = 'A', M = N, and if RANGE = 'I', M = IU-IL+1.
*
* W (output) DOUBLE PRECISION array, dimension (N)
* The first M elements contain the selected eigenvalues in
* ascending order.
*
* Z (output) DOUBLE PRECISION array, dimension (LDZ, max(1,M) )
* If JOBZ = 'V', then if INFO = 0, the first M columns of Z
* contain the orthonormal eigenvectors of the matrix A
* corresponding to the selected eigenvalues, with the i-th
* column of Z holding the eigenvector associated with W(i).
* Note: the user must ensure that at least max(1,M) columns are
* supplied in the array Z; if RANGE = 'V', the exact value of M
* is not known in advance and an upper bound must be used.
*
* LDZ (input) INTEGER
* The leading dimension of the array Z. LDZ >= 1, and if
* JOBZ = 'V', LDZ >= max(1,N).
*
* ISUPPZ (output) INTEGER array, dimension ( 2*max(1,M) )
* The support of the eigenvectors in Z, i.e., the indices
* indicating the nonzero elements in Z. The i-th eigenvector
* is nonzero only in elements ISUPPZ( 2*i-1 ) through
* ISUPPZ( 2*i ).
********** Implemented only for RANGE = 'A' or 'I' and IU - IL = N - 1
*
* WORK (workspace/output) DOUBLE PRECISION array, dimension (LWORK)
* On exit, if INFO = 0, WORK(1) returns the optimal (and
* minimal) LWORK.
*
* LWORK (input) INTEGER
* The dimension of the array WORK. LWORK >= 20*N.
*
* If LWORK = -1, then a workspace query is assumed; the routine
* only calculates the optimal size of the WORK array, returns
* this value as the first entry of the WORK array, and no error
* message related to LWORK is issued by XERBLA.
*
* IWORK (workspace/output) INTEGER array, dimension (LIWORK)
* On exit, if INFO = 0, IWORK(1) returns the optimal (and
* minimal) LIWORK.
*
* LIWORK (input) INTEGER
* The dimension of the array IWORK. LIWORK >= 10*N.
*
* If LIWORK = -1, then a workspace query is assumed; the
* routine only calculates the optimal size of the IWORK array,
* returns this value as the first entry of the IWORK array, and
* no error message related to LIWORK is issued by XERBLA.
*
* INFO (output) INTEGER
* = 0: successful exit
* < 0: if INFO = -i, the i-th argument had an illegal value
* > 0: Internal error
*
* Further Details
* ===============
*
* Based on contributions by
* Inderjit Dhillon, IBM Almaden, USA
* Osni Marques, LBNL/NERSC, USA
* Ken Stanley, Computer Science Division, University of
* California at Berkeley, USA
*
* =====================================================================
*
* .. Parameters ..